Literature DB >> 17625382

Influence of movement on FP-CIT SPECT quantification: a Monte Carlo based simulation.

Walter Koch1, Mona Mustafa, Christian Zach, Klaus Tatsch.   

Abstract

AIM: Head motion during acquisition is a frequently observed phenomenon while imaging the brain with SPECT. The aim of this study was to obtain detailed insight into the effects of head motion on the specific striatal binding of I-FP-CIT based on Monte Carlo simulations.
MATERIALS AND METHODS: Based on the Monte Carlo code and the digital Zubal phantom, different movement profiles (angular movement in the transaxial and sagittal plane ranging from -10 to +10 degrees) were systematically simulated for normal striatal binding and neurodegeneration. A triple-headed SPECT camera equipped with low-energy, high-resolution, parallel-hole collimators was modelled for this purpose. The projection data were reconstructed iteratively and the images were then evaluated using fully automated quantification software based on morphology guided volumes of interest. In addition, data were evaluated with a method taking into account partial volume effects.
RESULTS: Simulated movement resulted in blurring and streaking of the striatal structures with a concomitant change in measured specific striatal binding in most simulated profiles ranging from -44% to +2% (for the morphology guided volume of interest analyses) and -23% to +28% (for the method intended to overcome partial volume effects). In contrast to angular movement in the sagittal plane, rotation in the transaxial plane caused left/right asymmetry up to 41%. In the simulation of neurodegeneration, almost all movement profiles lead to an increase of putamen-to-caudate ratios.
CONCLUSIONS: Motion during the acquisition of a SPECT scan can have an important impact on measured dopamine transporter binding with its extent varying in dependency on the method of analysis used. While this is of prime importance in a research setting, it can also have implications in clinical routine imaging.

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Year:  2007        PMID: 17625382     DOI: 10.1097/MNM.0b013e328273bc6f

Source DB:  PubMed          Journal:  Nucl Med Commun        ISSN: 0143-3636            Impact factor:   1.690


  3 in total

1.  Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics.

Authors:  Markus Wenzel; Fausto Milletari; Julia Krüger; Catharina Lange; Michael Schenk; Ivayla Apostolova; Susanne Klutmann; Marcus Ehrenburg; Ralph Buchert
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-08-31       Impact factor: 9.236

2.  Is absolute quantification of dopaminergic neurotransmission studies with 123I SPECT ready for clinical use?

Authors:  Habib Zaidi; Georges El Fakhri
Journal:  Eur J Nucl Med Mol Imaging       Date:  2008-07       Impact factor: 9.236

3.  Optimization of imaging parameters for SPECT scans of [99mTc]TRODAT-1 using Taguchi analysis.

Authors:  Cheng-Kai Huang; Jay Wu; Kai-Yuan Cheng; Lung-Kwang Pan
Journal:  PLoS One       Date:  2015-03-19       Impact factor: 3.240

  3 in total

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